Grey prediction model of electric vehicle motor based on particle swarm optimization

Due to various motor faults in electric vehicles, a large amount of data are usually required in reliability analysis. In order to accurately predict the malfunction time, we established a fault tree model of components with high failure rate, and proposed an analytic formula. Grey algorithm based on small sample data was introduced into the reliability calculation of motor, and the traditional model and improved model were simulated and analyzed. To further improve the prediction accuracy, particle swarm optimization (PSO) which has the abilities of seeking the global optimum was utilized to fit the two grey models aiming at least mean squared errors, and to predict the malfunction time. At last, the optimization modelwas validated by two sets of measured data.The analysis results reveal that, the average relative errors of optimization algorithm are 3.36% and 5.05%, repectively, and the maximum relative errors are 5.62% and 8.41%, repectively. The results verify the effectiveness of the proposed algorithm, which provides fundamental basis for faults prediction of motors used in electric vehicle.